Acceptance in Blame: How and why we Blame the Victims of Street Harassment
Bibliographic record
Abstract
Globally, and on a daily basis, women are subjected to unwanted verbal and/or physical intrusions such as catcalling, leering, honking, sexually explicit or sexist comments, touching or grabbing, amongst other actions that are all considered street harassment. This paper is a review of some of the literature available, which focuses on the psychological and feminist aspects of street harassment and victim blaming through social, cognitive, intersectional, and economic lenses. Regarding psychological theories, I will examine reasons why victim blaming happens through the theories of the just-world hypothesis, cognitive dissonance, and the bystander effect. The feminist theories touch on the basics of objectification and power dynamics found within gender, which can help us understand why street harassment happens. Lastly, I will emphasize the importance of starting a conversation about the pervasiveness of street harassment and victim blaming, and why it is important to know where the blame should be instead of where it is almost always placed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".